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Brand value has become an important corporate performance metric, as can be observed from the close following of the annual Top 100 Brand Values ranked by brand consultancy firm Interbrand and reported in Business Week. In this study, we employ a simultaneous equations model to examine the non-linear influence of lagged advertising, marketing promotions and R&D expenses on brand value after controlling for net income and lagged brand valuation. We infer that these lagged expenses yield diminishing returns to brand value. The effect of R&D expense is the weakest, possibly because it is confounded with the advertising and promotional effects. Differences across industry segments or country base are not statistically significant. Copyright Springer Science + Business Media, LLC 2006Brand value, Advertising, Promotions, R&D,

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The admission of emergency patients in a hospital is unscheduled, urgent, and takes priority over elective patients, who are usually scheduled several days in advance. Hospital beds are a critical resource, and the management of elective admissions by enforcing quotas could reduce incidents of shortfall. We propose a distributionally robust optimization approach for managing elective admissions to determine these quotas. Based on an ambiguous set of probability distributions, we propose an optimized budget of variation approach that maximizes the level of uncertainty the admission system can withstand without violating the expected bed shortfall constraint. We solve the robust optimization model by deriving a second order conic problem (SOCP) equivalent of the model. The proposed model is tested in simulations based on real hospital admission data, and we report favorable results for adopting the robust optimization models.

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In introducing analytic queuing models, textbooks rarely justify the assumption of the exponential and the Poisson distributions. This paper demonstrates how a real-life phenomenon familiar to many students, namely the occurrences of soccer goals, can drive the ideas. An Excel worksheet is made available for further analysis.

The paper reports some initiatives to freshen up the typical undergraduate business forecasting course. These include (1) students doing research and presentations on contemporary tools and industry practices such as neural networks and collaborative forecasting (2) insertion of Logistic Regression in the curriculum (3) productive use of applets available on the Internet to convey abstract concepts underlying ARIMA models and (4) showcasing forecasting tools in timely or familiar applications. These initiatives align with the best practices framed across the "Making Statistics More Effective in Schools of Business" (MSMESB) conferences. Course experiences and student feedback are also discussed.